WITHDRAWN: A meta-analysis on associations of CDKN2B-AS variants with atherosclerotic cardio-cerebral vascular diseases

Association between lncRNA ANRIL genetic variants with the susceptibility to ischemic stroke: From a case-control study to meta-analysis

BACKGROUND

Recent studies have reported that lncRNA (long noncoding RNAs) antisense non-coding RNA in the INK4 locus (ANRIL) plays important roles in the development of atherosclerosis through regulating cell apoptosis, proliferation, and adhesion. GWAS (genome-wide association studies) identified common genetic variants within ANRIL could confer risk of ischemic stroke (IS) in southern Sweden.

METHODS

We performed a case-control study, including 567 IS patients and 552 healthy controls from unrelated northern Chinese Han population, aiming to explore the association between lncRNA ANRIL rs2383207, rs4977574 polymorphisms and the risk of IS. Subsequently we implemented a meta-analysis to further assess the relationship of these variants and the disease.

RESULTS

In our case-control study, no significant associations were observed in all models between above 2 polymorphisms and IS. Next in our subgroup analysis, we detected significant association between GA genotype of rs4977574 and the increased risk of LAA-IS (large-artery atherosclerotic ischemic stroke), similar elevated risk also appeared in the GG + GA genotype under the dominant model (P = .048, OR = 1.385, 95% CIs 1.002-1.914; P = .040, OR = 1.378, 95% CIs 1.015-1.872, respectively). As for rs2383207, negative results were obtained under all models and subgroups. Our meta-analysis showed a significant association between rs4977574 polymorphism and IS risk in allele model (G vs A P = .002, OR = 1.137, 95% CIs 1.048-1.234); with respect to rs2383207 polymorphism, no significant association between that and the risk of IS was detected under the dominant model (GA + AA vs GG, P = .061, OR = 0.923, 95% CIs 0.849-1.004), or recessive model (AA vs GA + GG, P = .656, OR = 0.972, 95% CIs 0.858-1.101), or allele model (A vs G, P = .326, OR = 0.952, 95% CIs 0.863-1.050). Likewise, no significant association between rs2383207 and IS was found in different stoke subtypes (P > .05).

CONCLUSIONS

Our findings indicated G allele of lncRNA ANRIL rs4977574 could increase the risk of IS, and the variant may be associated with susceptibility to LAA-IS in Chinese Han population.

Long non-coding RNA ANRIL polymorphisms in papillary thyroid cancer and its severity

Background: Long non-coding RNAs are likely to have a role in the pathogenesis of many diseases, including cancer. We hypothesised an effect of certain ANRIL single nucleotide polymorphisms (SNPs) in papillary thyroid cancer. Methods: Genomic ANRIL SNPs in rs11333048, rs4977574, rs1333040 and rs10757274 were determined in 134 papillary thyroid cancer patients and 155 age- and sex-matched controls. Results: None of the ANRIL SNPs were individually linked to papillary thyroid cancer. However, the AAAC haplotype (A from rs11333048, A from rs4977574, A from rs1333040 and C from rs10757274, respectively) showed a protective effect from papillary thyroid cancer whilst the CAAC and CAGT haplotypes were associated with cancer. The rs1333048 CC variant was more frequent in patients with larger tumour size (≥1 cm) in a recessive model (OR 3.4 [95%CI, 1.1-11], P = 0.035). The rs4977574 AC variant was associated with smaller tumour size in an over-dominant model (OR 0.4 [95%CI, 0.2-1.0], P = 0.041). SNPs in rs10757274 (AA: p = 0.045) and rs1333040 (CC: p = 0.019) are linked to a lower likelihood of III-IV cancer stages in dominant or codominant models. Conclusions: Certain haplotypes of ANRIL SNPs are associated with papillary thyroid cancer. ANRIL rs1333048 and rs4977574 variants were associated with larger and smaller tumour sizes, respectively. rs10757274 and rs1333040 variants might lead to lower III-IV cancer stages. These SNPs may be important in the diagnosis of this form of thyroid cancer.

Exploring gene-gene interaction in family-based data with an unsupervised machine learning method: EPISFA

Gene-gene interaction (G × G) is thought to fill the gap between the estimated heritability of complex diseases and the limited genetic proportion explained by identified single-nucleotide polymorphisms. The current tools for exploring G × G were often developed for case-control designs with less considerations for their applications in families. Family-based studies are robust against bias led from population stratification in genetic studies and helpful in understanding G × G. We proposed a new algorithm epistasis sparse factor analysis (EPISFA) and epistasis sparse factor analysis for linkage disequilibrium (EPISFA-LD) based on unsupervised machine learning to screen G × G. Extensive simulations were performed to compare EPISFA/EPISFA-LD with a classical family-based algorithm FAM-MDR (family-based multifactor dimensionality reduction). The results showed that EPISFA/EPISFA-LD is a tool of both high power and computational efficiency that could be applied in family designs and is applicable within high-dimensionality datasets. Finally, we applied EPISFA/EPISFA-LD to a real dataset drawn from the Fangshan/family-based Ischemic Stroke Study in China. Five pairs of G × G were discovered by EPISFA/EPISFA-LD, including three pairs verified by other algorithms (FAM-MDR and logistic), and an additional two pairs uniquely identified by EPISFA/EPISFA-LD only. The results from EPISFA might offer new insights for understanding the genetic etiology of complex diseases. EPISFA/EPISFA-LD was implemented in R. All relevant source code as well as simulated data could be freely downloaded from https://github.com/doublexism/episfa.

Long noncoding RNA CDKN2B-AS1 interacts with transcription factor BCL11A to regulate progression of cerebral infarction through mediating MAP4K1 transcription

The effective therapeutic approach of cerebral infarction is limited because of its underlying complexity. Recently, multiple long noncoding RNAs (lncRNAs) have been identified in the pathogenesis of cerebral infarction. Here, the current study aims to explore the interaction among lncRNA cyclin-dependent kinase inhibitor-2B-antisense RNA 1 (CDKN2B-AS1), transcription factor B-cell lymphoma/leukemia 11A (BCL11A), and MAPKK kinase kinase 1 (MAP4K1) and further investigate whether they affect cerebral infarction progression. The expression of CDKN2B-AS1, BCL11A, and MAP4K1 was altered in lymphocytes extracted from patients with cerebral infarction. In order to identify their roles in regulatory T (Treg) cells, the proliferation and apoptosis of the CD4+CD25+ Treg cells were examined, and levels of IL-4, IL-10, and TGF-β were determined. Also, the RNA crosstalk among CDKN2B-AS1, BCL11A, and MAP4K1 was validated. Finally, we established a rat model of middle cerebral arterial occlusion to evaluate the neurologic impairment and cerebral infarction volume. The results revealed that lymphocytes in patients with cerebral infarction presented with the up-regulated expression of CDKN2B-AS1. Moreover, BCL11A could specifically bind to CDKN2B-AS1 and MAP4K1 promoter so as to inhibit MAP4K1. Moreover, it was observed that down-regulated CDKN2B-AS1 inhibited CD4+CD25+ Treg-cell proliferation, reduced levels of IL-4, IL-10, and TGF-β and cerebral infarction volume, and elevated MAP4K1 expression. Collectively, our study provides evidence that CDKN2B-AS1 silencing could increase MAP4K1 expression to inhibit the CD4+CD25+ Treg-cell proliferation by reducing enrichment of transcription factor BCL11A, thereby protecting against cerebral infarction progression, highlighting a promising therapeutic strategy for treating cerebral infarction.-Lei, J.-J., Li, H.-Q., Mo, Z.-H., Liu, K.-J., Zhu, L.-J., Li, C.-Y., Chen, W.-L., Zhang, L. Long noncoding RNA CDKN2B-AS1 interacts with transcription factor BCL11A to regulate progression of cerebral infarction through mediating MAP4K1 transcription.